Generative AI in Finance: Forecasting, Fraud & Regulation Training Course

Fundamentals of Generative AI

• An introduction to generative models and their significance in the financial sector
• Key model types: LLMs, GANs, and VAEs
• Understanding the strengths and constraints of these models in finance

Applying Generative Adversarial Networks (GANs) to Finance

• Mechanisms of GANs: the interplay between generators and discriminators
• Leveraging GANs for synthetic data creation and fraud simulation
• Practical example: producing realistic transaction data for testing purposes

Leveraging Large Language Models (LLMs) and Prompt Engineering

• How LLMs process and produce financial documentation
• Formulating prompts for predictive analysis and risk assessment
• Key applications: summarising financial reports, KYC verification, and detecting warning signs

Financial Forecasting via Generative AI

• Enhancing time series predictions with hybrid LLM and ML models
• Creating scenarios and conducting stress tests
• Practical example: predicting revenue by combining structured and unstructured data

Fraud Detection and Anomaly Recognition

• Employing GANs to spot anomalies in transactional data
• Uncovering new fraud trends through LLM-driven prompt workflows
• Assessing model performance: distinguishing false positives from genuine risk signals

Regulatory and Ethical Considerations

• Ensuring explainability and transparency in AI-generated outputs
• Addressing risks associated with model hallucinations and bias in finance
• Meeting regulatory requirements (e.g., GDPR, Basel guidelines)

Developing Generative AI Use Cases for Financial Institutions

• Constructing compelling business cases for internal adoption
• Striking a balance between innovation and risk/compliance obligations
• Establishing governance frameworks for responsible AI deployment

Recap and Future Directions